融合多模态特征的社会多媒体谣言检测技术研究

• Published in: Nanjing Xinxi Gongcheng Daxue Xuebao Vol. 9; no. 6; pp. 583 - 592
• Main Authors: Jin, Zhiwei, Cao, Juan, Wang, Bo, Wang, Rui, Zhang, Yongdong
• Format: Journal Article
• Language: Chinese
• Published: Nanjing Nanjing University of Information Science & Technology 01.12.2017; 中国科学院计算技术研究所 智能信息处理实验室,北京,100190; 中国科学院大学,北京,100049%中国电子科技集团公司电子科学研究院创新中心,北京,100041
• Subjects: Digital media; Feature extraction; Gossip; Information dissemination; Media; Social networks; 多媒体计算; 多模态特征融合; deep learning; rumor detection; 新闻认证; multimodal feature fusion; 社会媒体计算; 深度学习; social media computing; news verification; multimedia computing; 谣言检测
• ISSN: 1674-7070
• DOI: 10.13878/j.cnki.jnuist.2017.06.003

Social media, such as microblogs, has developed rapidly nowadays, which accelerates the information diffusion on the Internet. However,numerous false rumors fostered on social media are spreading widely on the social network and can result in serious consequences.It has become a huge concern in research and industry areas to detect rumors automatically on social media. Focused on the rumor detection task, this paper summarizes the approaches of multimodal fusion on this problem. Starting from the basic concepts, we give formal definitions of rumors and introduce the characteristics of social media. We summarize the studies on rumor detection into two major parts, i. e., extracting effective multimodal features to identify rumors and constructing robust models to detect rumors. For each of the research aspects,we give detailed introduction based on existing studies. This paper can be served as a basic guidance to build state-of-the-art rumor detection models and a reference for future researches.